Modeling the Causal Relationships of Country Risk Factors Using Expert Experience and Intuition-based Sensing Data
Year: 2025
Type: article
Abstract: Structural Equation Modeling (PLS-SEM), spatial differencesInternational construction projects inherently involve risks that fundamentally differ from those encountered in domestic projects due to variations in legal, political, and economic environments.Given the confidentiality and data limitations of such projects, this study introduces an innovative approach by conceptualizing expert judgment as sensor-like data to compensate for the absence of direct measurements.Specifically, Expert-Driven Experience and Intuition-Based Sensing Data are treated as qualitative proxies for sensor input, enabling the extraction of structured knowledge from tacit insights.These data are integrated with Partial Least Squares Structural Equation Modeling to quantitatively analyze the organic interactions and causal relationships among country-specific risk factors and their impact on cost overruns.The proposed framework demonstrates enhanced reliability and robustness, even in dataconstrained contexts.The empirical analysis reveals that 'Environmental' risk exerts the most significant direct effect on cost overruns (standardized path coefficient = 0.360), whereas 'Legal' risk has the smallest indirect impact (0.067).The results emphasize the importance of economic and environmental risks, offering a foundation for developing targeted risk management strategies to ensure the financial stability of construction projects.
Source: Sensors and Materials
DOI: 10.18494/sam5596
Authors: Seoungbeom Na, Chang Geun Lee, Woosik Jang, Inju Hwang, Hongcheol Lee
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $748
OpenAlex ID: W7083323905
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